Dr. Abdel-Khalik is an Associate Professor at the Department of Nuclear Engineering at Purdue University. He completed his B.S.N.E at the University of Alexandria, Egypt in 2000 followed by his M.S. (2002) and Ph.D (2004) in Nuclear Engineering at the North Carolina State University in Raleigh, NC. He has worked at AREVA and North Carolina State University before joining Purdue in 2014. His primary areas of interest include Mathematical Inference, Reduced Order Modeling, Uncertainty Quantification, Big Data Analytics with primary focus on nuclear energy systems

Dr. Raj is a post-doctoral researcher who obtained his B.S in Mechanical engineering from IIT Kanpur in Kanpur, India. He completed his Masters and Ph.D. in Nuclear Engineering at the North Carolina State University in Raleigh, NC. He possesses over five years of expertise in the field of computational material science, with his most notable contributions in the development of computational tools for atomistic simulations as well as machine learning models for automated in-situ quality assurance. His current research interests include modeling of materials in extreme environments, nanoscale energy transport mechanisms, additive manufacturing, and reinforcement learning.

Dr. Sundaram is a post-doctoral researcher who obtained his B.S and Ph.D. in Nuclear Engineering from Purdue University in West Lafayette, IN. He possesses over three years of expertise in developing embedded intelligence for industrial control systems, focusing on cybersecurity and data masking applications. His current research interests include active software/hardware fingerprinting, applying information-theory to inference, quantifying component redundancies in advanced reactor designs, and data self-healing through active remediation.

Tarikul Islam is a first-year Ph.D. student who completed his B.S.N.E at the University of Dhaka in Dhaka, Bangladesh. His research interests lie in machine learning, computational reactor physics. and mathematical modeling of nuclear phenomena.

Richard Johnson is a first year master's student who completed his B.S.N.E at Purdue University. His research interests are in the fields of reduced-order modeling, synthetic data generation, and time-series modeling. His work focuses on applying data analytics on time-series data for forecasting and cybersecurity applications.

Tyler Lewis is a first-year Master's student who obtained his B.S.N.E from Purdue University in West Lafayette, IN. His research interests are in generating surrogate data, data masking, and development of software plugins for anomaly detection in nuclear reactors. His work currently focuses on the extraction of macroscopic and microscopic trends from data and evaluating their relevance using information-theoretic measures.

Charles Owen is a first year master's student who obtained his B.S.N.E. from Purdue University in West Lafayette, IN. His research interests are in modeling inverse problems, regression, and surrogate modeling. His work currently focuses on identifying operational parameters directly from time-series data and predicting the mechanical properties of 3D-printed samples based on initial parameters and image data.

Jeongwon Seo is a fourth-year Ph.D. student who completed his B.S. in Applied Chemistry at the Korean Military Academy in Seoul, South Korea, followed by his M.S. in Nuclear Physics at the Moscow State University in Moscow, Russia. His current research delves into the areas of constrained sensitivity analysis and uncertainty quantification.

Shiming Yin is a first-year Ph.D. student who obtained his B.S.N.E from Wuhan University in Wuhan, China, and his M.S in Nuclear Science and Technology at Tsinghua University in Beijing, China. His research interests are primarily in the field of uncertainty quantification and modeling validation of energy systems.

Latif Adurzada is a sophomore undergraduate student pursuing his B.S Mathematics and Statistics at Purdue University. His research interest is application of reinforcement learning to create real-time adaptive algorithms. Latif’s work focuses on training a RL agent to analyze in-situ data and calibrate parameters to achieve micro-level control over the melted materials.

Michael Solmos is a sophomore undergraduate student pursuing his B.S.N.E at Purdue University. His research interests include generating surrogate data for hybrid energy systems and for AI uses. He primarily researches time series analysis techniques and their applications toward synthetic data generation.

Chloe Yoder is a junior undergraduate student pursuing her B.S.N.E. at Purdue University. Her research interests are in the field of reduced-order modeling, data masking, and utilizing machine-learning to prevent reverse-engineering of proprietary information. Her work focuses on using mutual information and decomposition techniques to generate synthetic data for AI.

Dr. Huang is an Assistant Professor at Southeast University in Nanjing, China. She obtained her Ph.D. in Nuclear Engineering from Purdue University in West Lafayette, IN. She primarily works in the field of uncertainty quantification and reduced-order modeling. Her key contributions include the development of cross-section uncertainty libraries for core simulations, and the physics-guided coverage mapping (PCM) methodology for model validation.

Dr. Li is an Argonne Fellow at Argonne National Laboratory in Lemont, IL. She obtained her Ph.D. in Nuclear Engineering from Purdue University in West Lafayette, IN. Her research primarily focuses on designing reduced-order surrogate models for industrial processes, and passive anomaly-detection techniques for nuclear reactor cybersecurity. Her work provides an additional layer of defense at the process level in the event that traditional IT defenses are bypassed by a malicious user.

Dr. Zhou is a post-doctoral fellow at Argonne National Laboratory in Lemont, IL. She obtained her Ph.D. in Nuclear Engineering from Purdue University in West Lafayette, IN.. Her research focuses on sensitivity analysis and uncertainty quantification in nuclear reactors. She has also contributed to the development of data-driven methods to analyze and optimize energy portfolios in conjunction with Idaho National Lab, and the physics-guided coverage mapping (PCM) methodology for model validation.